{
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   "cell_type": "code",
   "execution_count": 2,
   "id": "82a1df45-b253-4d98-aed4-55dfb5e906a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting xlrd\n",
      "  Downloading xlrd-2.0.1-py2.py3-none-any.whl.metadata (3.4 kB)\n",
      "Downloading xlrd-2.0.1-py2.py3-none-any.whl (96 kB)\n",
      "   ---------------------------------------- 0.0/96.5 kB ? eta -:--:--\n",
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      "   ----------------------------- ---------- 71.7/96.5 kB ? eta -:--:--\n",
      "   ---------------------------------------- 96.5/96.5 kB 78.9 kB/s eta 0:00:00\n",
      "Installing collected packages: xlrd\n",
      "Successfully installed xlrd-2.0.1\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install xlrd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a7a2dcdb-35d5-4b88-8877-f9eb4c53b997",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "信计221 韩梦瑶 224180106\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from pyecharts import options as opts\n",
    "from pyecharts.charts import Candlestick\n",
    "# 读取数据\n",
    "df = pd.read_excel(\"中国平安.xls\", sheet_name=\"Sheet1\")\n",
    "# 数据预处理\n",
    "df['date'] = pd.to_datetime(df['date']).dt.strftime('%Y-%m-%d')\n",
    "data = df[['open', 'high', 'low', 'close']].values.tolist()\n",
    "# 创建K线图\n",
    "c = (\n",
    "    Candlestick(init_opts=opts.InitOpts(width=\"1200px\", height=\"600px\"))\n",
    "    .add_xaxis(xaxis_data=df['date'].tolist())\n",
    "    .add_yaxis(\n",
    "        series_name=\"\",\n",
    "        y_axis=data,\n",
    "        itemstyle_opts=opts.ItemStyleOpts(\n",
    "            color=\"#ef232a\",  # 阴线颜色\n",
    "            color0=\"#14b143\", # 阳线颜色\n",
    "            border_color=\"#ef232a\",\n",
    "            border_color0=\"#14b143\"\n",
    "        ),\n",
    "    )\n",
    "    .set_global_opts(\n",
    "        title_opts=opts.TitleOpts(\n",
    "            title=\"中国平安2020年上半年股价K线图\",\n",
    "            subtitle=\"K线图\",\n",
    "            pos_left=\"center\"\n",
    "        ),\n",
    "        xaxis_opts=opts.AxisOpts(\n",
    "            type_=\"category\",\n",
    "            axislabel_opts=opts.LabelOpts(rotate=45),\n",
    "            splitline_opts=opts.SplitLineOpts(is_show=False)\n",
    "        ),\n",
    "        yaxis_opts=opts.AxisOpts(\n",
    "            min_=66,\n",
    "            max_=84,\n",
    "            splitline_opts=opts.SplitLineOpts(\n",
    "                is_show=True,\n",
    "                linestyle_opts=opts.LineStyleOpts(\n",
    "                    type_=\"dashed\",\n",
    "                    opacity=0.5\n",
    "                )\n",
    "            )\n",
    "        )\n",
    "    )\n",
    ")\n",
    "# 渲染生成HTML文件\n",
    "c.render(\"china_pingan_kline.html\")\n",
    "print(\"信计221 韩梦瑶 224180106\")"
   ]
  },
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   "execution_count": 6,
   "id": "35aa7417-9bca-4352-bf08-f3db7ab831cb",
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     "text": [
      "信计221 韩梦瑶 224180106\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import plotly.graph_objects as go\n",
    "# 读取数据\n",
    "df = pd.read_excel(\"中国平安.xls\")\n",
    "df['date'] = pd.to_datetime(df['date'])  # 转换日期格式\n",
    "# 绘制OHLC图\n",
    "fig = go.Figure(data=go.Ohlc(\n",
    "    x=df['date'],\n",
    "    open=df['open'],\n",
    "    high=df['high'],\n",
    "    low=df['low'],\n",
    "    close=df['close'],\n",
    "    increasing_line_color='green',  # 阳线颜色\n",
    "    decreasing_line_color='red'  # 阴线颜色\n",
    "))\n",
    "# 设置图表布局\n",
    "fig.update_layout(\n",
    "    title=\"中国平安2020年上半年股价OHLC图\",\n",
    "    xaxis_title=\"日期\",\n",
    "    yaxis_title=\"价格\",\n",
    "    template=\"plotly_white\"  # 替换为 Plotly 支持的模板名称\n",
    ")\n",
    "# 显示图表\n",
    "fig.show()\n",
    "print(\"信计221 韩梦瑶 224180106\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9ce04081-0b2e-4bcc-b0c0-ec687dc5b103",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting mplfinance\n",
      "  Downloading mplfinance-0.12.10b0-py3-none-any.whl.metadata (19 kB)\n",
      "Requirement already satisfied: matplotlib in c:\\anaconda\\lib\\site-packages (from mplfinance) (3.8.4)\n",
      "Requirement already satisfied: pandas in c:\\anaconda\\lib\\site-packages (from mplfinance) (2.2.3)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in c:\\anaconda\\lib\\site-packages (from matplotlib->mplfinance) (1.2.0)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\anaconda\\lib\\site-packages (from matplotlib->mplfinance) (0.11.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in c:\\anaconda\\lib\\site-packages (from matplotlib->mplfinance) (4.51.0)\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in c:\\anaconda\\lib\\site-packages (from matplotlib->mplfinance) (1.4.4)\n",
      "Requirement already satisfied: numpy>=1.21 in c:\\anaconda\\lib\\site-packages (from matplotlib->mplfinance) (1.26.4)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\anaconda\\lib\\site-packages (from matplotlib->mplfinance) (23.2)\n",
      "Requirement already satisfied: pillow>=8 in c:\\anaconda\\lib\\site-packages (from matplotlib->mplfinance) (10.3.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in c:\\anaconda\\lib\\site-packages (from matplotlib->mplfinance) (3.0.9)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in c:\\anaconda\\lib\\site-packages (from matplotlib->mplfinance) (2.9.0.post0)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\anaconda\\lib\\site-packages (from pandas->mplfinance) (2024.1)\n",
      "Requirement already satisfied: tzdata>=2022.7 in c:\\anaconda\\lib\\site-packages (from pandas->mplfinance) (2023.3)\n",
      "Requirement already satisfied: six>=1.5 in c:\\anaconda\\lib\\site-packages (from python-dateutil>=2.7->matplotlib->mplfinance) (1.16.0)\n",
      "Downloading mplfinance-0.12.10b0-py3-none-any.whl (75 kB)\n",
      "   ---------------------------------------- 0.0/75.0 kB ? eta -:--:--\n",
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      "   -------------------------------- ------- 61.4/75.0 kB 113.0 kB/s eta 0:00:01\n",
      "   ---------------------------------------- 75.0/75.0 kB 129.6 kB/s eta 0:00:00\n",
      "Installing collected packages: mplfinance\n",
      "Successfully installed mplfinance-0.12.10b0\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install mplfinance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "11fd8359-1077-4500-afb7-7c22b2c77e69",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SimHei\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.font_manager as fm\n",
    "for font in fm.fontManager.ttflist:\n",
    "    if 'SimHei' in font.name or 'Noto Sans CJK' in font.name:\n",
    "        print(font.name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9d8b10df-40b2-46ec-be7f-aa2459cab9e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "信计221 韩梦瑶 224180106\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import mplfinance as mpf\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import font_manager\n",
    "# 配置中文字体\n",
    "font_path = 'C:/Windows/Fonts/msyh.ttc'  # 微软雅黑字体路径（根据你的系统调整路径）\n",
    "font_prop = font_manager.FontProperties(fname=font_path)\n",
    "# 读取股票数据\n",
    "df = pd.read_excel(\"中国平安.xls\", engine='xlrd')\n",
    "# 确保日期列是正确的datetime格式\n",
    "df['date'] = pd.to_datetime(df['date'])\n",
    "# 筛选出2020年上半年（1月1日至6月30日）的数据\n",
    "df = df[(df['date'] >= '2020-01-01') & (df['date'] <= '2020-06-30')]\n",
    "# 设置日期为索引\n",
    "df.set_index('date', inplace=True)\n",
    "# 筛选所需的列：Open, High, Low, Close\n",
    "df = df[['open', 'high', 'low', 'close']]\n",
    "# 定义黑白相间的自定义样式\n",
    "renko_style = mpf.make_mpf_style(base_mpf_style='classic', rc={'font.family': font_prop.get_name()})\n",
    "# 使用自定义样式绘制 Renko 图，并设置纵坐标范围\n",
    "mpf.plot(df, type='renko', style=renko_style, title=\"2020年上半年中国平安股票价格 Renko 图\", ylabel=\"股票价格\", \n",
    "         ylim=(65.0, 87.5), savefig=\"renko_chart.png\")\n",
    "\n",
    "# 如果需要的话，将图像嵌入到HTML中\n",
    "html_code = '''\n",
    "<html>\n",
    "<head><title>Renko 图</title></head>\n",
    "<body>\n",
    "<h1>2020年上半年中国平安股票价格 Renko 图</h1>\n",
    "<img src=\"renko_chart.png\" alt=\"Renko 图\">\n",
    "</body>\n",
    "</html>\n",
    "'''\n",
    "with open(\"renko_chart.html\", \"w\", encoding=\"utf-8\") as f:\n",
    "    f.write(html_code)\n",
    "print(\"信计221 韩梦瑶 224180106\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7400dca5-b2b6-4826-b9bd-60ad830aec5f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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